Human tumors rely on defective protein-based cell signaling processes, driven by post-translational modifications such as protein phosphorylation, to grow, survive and metastasize. These signaling networks are also the targets for most of the current and planned molecular targeted inhibitors. An example is HERCEPTIN, a drug that can block the hyperactive Epidermal Growth Factor (EGF) signaling system in breast cancer. Only patients that have this signaling pathway over-expressed and activated respond to the therapy. An urgent critical need for patient care is to identify patients which will respond to standard therapy and who will require more aggressive therapeutic measures. These more aggressive measures almost always come with increased morbidity, thus are not selected a priori without justification. For example, women with node negative breast cancer and who have estrogen receptor on their tumors are eligible for tamoxifen therapy. However, there are about 30-40% of women who will not respond to tamoxifen therapy, and would require more aggressive treatment—for example with an aromatase inhibitor (AI). Aromatase inhibitors, however, are associated with moderate to severe bone loss, so giving all women AI therapy would be unacceptable. A biomarker that could discriminate outcome and response to therapy would be of great benefit for this example cohort. This conundrum is common to most all of the other human cancers: discrimination of a population that would respond to standard of care from those with poorer prognosis.
Gene expression analysis has indicated an ability to derive prognostic signatures for outcome; however, these endpoints are limited to simple stratification only. The signature cannot tell the physician how to treat the non-responder group; it simply can be used to decide who will respond and who won't. By contrast, protein-signaling profiling can provide a prognostic signature and, importantly, provide information on what therapies to treat the non-responder cohort with. This is because the proteomic portraits are constructed on the drug targets themselves. Furthermore, the analysis of the many genes in gene expression analysis is complex, and generally involves the use of algorithms and extensive computer analysis and does not reflect the activated or functional state of the protein drug targets. Gene expression does not correlate with phosphorylation of signal pathway proteins.